SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output-Reference-Cited by-同舟云学术

SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output

Published:2023-01 Issue: Volume:262 Page:125375
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Chen Junxiong,Zhang Yu,Wu Ji,Cheng Weisong,Zhu Qiao

Funder

National Natural Science Foundation of China

Sichuan Province Science and Technology Support Program

International Science and Technology Cooperation Program of Sichuan Province

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference46 articles.

1. A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries;Luo;J Energy Chem,2022

2. Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: A critical comprehensive survey;Yang;J Energy Storage,2021

3. A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems;Wang;Renew Sustain Energy Rev,2020

4. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries;Ng;Appl Energy,2009

5. A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery;Zhang;Sustain Energy Technol Assess,2020

Cited by 108 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A hybrid deep learning framework integrating bidirectional sliding windows, TCN, and external attention for accurate state-of-charge estimation in lithium-ion batteries;Journal of Power Sources;2024-12

2. Optimizing efficiency of proton exchange membrane electrolyzer system based on multiphysics model and differential evolution strategy;Journal of Power Sources;2024-11

3. A novel battery SOC estimation method based on random search optimized LSTM neural network;Energy;2024-10

4. Mass estimation of tractor-semitrailer systems: An approach of dynamics and data fusion-driven in real environments;Measurement;2024-10

5. Discrete-time state-of-charge estimator for latent heat thermal energy storage units based on a recurrent neural network;Applied Energy;2024-10